Machine learning
Predictive machine learning is still changing outside chat and agents. Google's June TabFM release applies pretrained models to classification and regression on tables of data. Its reported benchmarks are worth testing against well-tuned conventional models. We have no evidence that one approach wins on every company dataset, or that all the value has moved to frontier reasoning.
Research snapshot · updated Nov 12, 2026
State of the art (2026)
Machine learning in mid-2026 is two fields. Classical ML – gradient boosting, tabular models, recommendation and fraud systems – is mature production infrastructure inside every large enterprise. The moving frontier is frontier-scale deep learning, where Anthropic (Claude Opus 4.x and Fable 5, released June 2026), OpenAI (GPT-5.x) and Google DeepMind (Gemini 3.x) lead on reasoning and agentic benchmarks, while open-weight models – DeepSeek V4, GLM-5, Qwen 3.x – have effectively closed the coding gap and now sit roughly three to six months behind the closed frontier. Competition is shifting from raw capability to reinforcement-learning post-training, tool-use, long-horizon agents, evaluation and deployment governance ahead of EU AI Act high-risk enforcement.
Core thesis
Machine learning is a mature tool for recommendation, fraud detection, pricing and operations, and classical methods still do most of the work on tabular data. Research attention has moved to deep learning, large language models and reinforcement learning. How widely businesses use it varies by size, sector and function. The US Census Bureau's Business Trends and Outlook Survey tracks AI use on those lines, but it changed its question wording in November 2025 and does not measure classical machine learning separately. We do not have a reliable figure for the share of companies that use it.
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Signal stack
Evidence stacked leading → lagging
10 signals
Signal stack
Evidence stacked leading → lagging
Technology-native KPIs
Metrics that predict trajectory, tracked over time
6 tracked
Technology-native KPIs
Metrics that predict trajectory, tracked over time
Landscape map
Who builds what, and who depends on whom
121 players · 6 layers
Landscape map
Who builds what, and who depends on whom
Catalyst calendar
Dated events that will move the position
3 ahead
Catalyst calendar
Dated events that will move the position
Technology roadmap
Milestones on the path to maturity
8 milestones
Technology roadmap
Milestones on the path to maturity
Watchlists
Companies, people and papers, each with a condition for removing it
20 · 20
Watchlists
Companies, people and papers, each with a condition for removing it
Decision frameworks
What our view means for your job
Locked
Decision frameworks
What our view means for your job
Thesis changelog
When our view changed, and why
9 updates
Thesis changelog
When our view changed, and why
Change our mind
3 disconfirming conditions
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The full signal stack, technology-native KPIs tracked over time, the landscape of who depends on whom, the dated catalyst calendar, decision frameworks for every desk, live watchlists and a record of every time our view on Machine learning has changed. All of it is in CanaryIQ.